User Interaction Based Community Detection in Online Social Networks

Online social networks (OSNs) provide an excellent platform to connect, share, communicate, and interact among different groups of people. Discovering meaningful communities based on the interactions of different people in a social network is an active research topic in recent years. However, existing interaction based community detection techniques either rely on the content analysis or only consider the underlying structure of the social network graph, while identifying communities in OSNs. As a result, these approaches fail to identify active communities, i.e., communities based on actual interactions rather than mere friendship. To alleviate the limitations of existing approaches, we propose a novel solution of community detection in OSNs. The key idea of our approach comes from the following observations: (i) the degree of interaction between each pair of users can widely vary, which we term as the strength of ties, and (ii) for each pair of users, the interactions with mutual friends, which we term the group behavior, play an important role to determine their belongingness to the same community. Based on these two observations, we propose an efficient solution to detect communities in OSNs that utilizes the interactions of every pair of users as well as the interactions among mutual friends in a social network. The detailed experimental study shows that our proposed algorithm significantly outperforms state-of-the-art techniques for both real and synthetic datasets.